Variational autoencoders [1]} employ variational inference [2]} as a form of regularisation of the parameters or weights during the training, in order to ensure that it has good properties for prediction [3]}, [4]}, [5]}, [6]}. Burda et. al [7]} described an importance weighted autoencoder that extended the variational autoencoder to use multiple samples to approximate more complex posteriors.
